Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

Abstract

Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

Set-Based Rare Variant Expression Quantitative Trait Loci in Blood and Brain from Alzheimer Disease Study Participants

“…The set-based method using SKAT-O allows for opposite effect directions of the constituent SNPs in the test; however, closer scrutiny of the individual SNPs is necessary to draw conclusions about the collective influence of rare variants on expression, as well as consistency of the effect direction across tissues. Our results, which were generated from analyses at the tissue level, do not account for patterns that are cell-type specific within the blood and brain, as we recently demonstrated for common individual variant eQTLs in these datasets [ 26 ]. In addition, it is unclear whether the set-based eQTL method applied in this study would behave similarly for rare (MAF < 0.01) and low-frequency (0.01 < MAF < 0.05) variants analyzed separately.…”

“…Our rare-eQTL gene-level and pathway-level results confirm the substantial immune and inflammatory component to AD. Significant gene-level rare eQTLs in the brain included several HLA region loci linked to AD by GWAS ( HLA-DRB1 and HLA-DRB5 [ 35 , 36 ]) and cell-type specific eQTL analysis ( HLA-DOB [ 26 ]). IL27 ( p = 1.69 × 10 −30 ) is a cytokine, and CARD17 ( p = 6.73 × 10 −13 ) encodes a regulatory protein of inflammasomes, which are responsible for the activation of inflammatory responses [ 77 ].…”

“…Primary microglia isolated from the brains of APP/PS1 mutant mice with ablated type-I interferon signaling have shown reduced levels of Aβ 1–42 [ 80 ]. In addition to being a significant pathway-level rare eQTL, FPR2 is also very significant eQTL in the blood ( p = 1.22 × 10 −240 ), and more specifically, in interferon and anti-bacterial cells ( p = 3.81 × 10 −17 ) [ 26 ]. It is involved in the uptake and clearance of Aβ and contributes to innate immunity and inflammation [ 81 ].…”

“…ADNI microarray gene expression data were normalized and log-transformed using limma [ 25 ]. ROSMAP RNA-seq data were normalized and then log-transformed using a previously described pipeline [ 26 ]. The log-transformed expression data were evaluated using surrogate variable analysis (SVA) [ 27 ] to obtain surrogate variables for global technical effects and hidden effects, which were included as covariates in the analysis models for eQTL discovery.…”

“…To determine whether both common variants and gene-level aggregated rare/low-frequency variants target expression of the same genes, we evaluated the overlap in significant gene-based cis-eQTLs with those involving common variants (MAF > 0.05) within 1 Mb of protein-coding genes that were obtained previously from the Framingham Heart Study (blood) and ROSMAP (brain) gene expression datasets [ 26 ]. These comparisons not only indicated which eGenes are regulated by rare and/or common variants, but also determined whether multiple variants can separately up- or down-regulate expression of the same gene.…”